Comparative genomics has had great success in revealing evolutionary mechanisms and in predicting the functionality of proteins and non-coding functional regions. Mass spectrometry-based proteomics generates new types of data on the composition and localization of protein complexes and the interactions between complexes and individual proteins. The comparative analysis of these data has great potential as a useful approach to the characterization of the organization of the cell protein machinery. We propose to develop computational and experimental strategies for cross-species proteomic analysis. In Aim 1, we will improve existing, and develop new, computational methods for the homology-based identification of proteins and protein complexes using mass spectrometry data. These methods will not rely on the availability of protein sequences in the current database and, thus, will enable the analysis of multiple organisms currently out of the scope of proteomics. They will also increase the sensitivity of protein complex identifications in organisms with sequenced genomes because of the robustness with respect to incorrect gene predictions, sequencing errors, splicing isoforms and polymorphisms. In Aim 2, we will compare the protein interaction data for available and de novo generated examples of "molecular machines" from different organisms. We will further derive common patterns of evolutionary events in terms of the changes in protein interaction graphs. The observed changes in the composition of protein complexes, the interactions between complexes and in the individual proteins will be correlated with changes at the level of protein's sequence and structure. Gene duplication events will be characterized in terms of the interaction pattern, such that we will seek to design approaches that generate meaningful functional predictions for the specific "molecular machines." In Aim 3, we will develop automated computational methods for the comparative analysis of protein-protein interaction networks from different organisms. The application of these methods to emerging large-scale data on protein-protein interaction networks from multiple species will enable the identification of conserved interaction sets, the understanding of functionality and robustness of "molecular machines" and the characterization of the functional role of individual proteins.